Malware is the fastest growing threat to information technology systems. Although a single absolute solution for defeating malware is improbable, a stacked arsenal against malicious software enhances the ability to maintain security and privacy. This research attempts to reinforce the anti-malware arsenal by studying a behavioral activity common to software – the use of handles. The characteristics of handle usage by benign and malicious software are extracted and exploited in an effort to distinguish between the two classes. An automated malware detection mechanism is presented that utilizes memory forensics, information retrieval and machine learning techniques. Experimentation with a malware dataset yields a malware detection rate of 91.4% with precision and recall of 89.8% and 91.1%, respectively.

Autonomous vehicles are equipped with numerous electronic control units (ECUs). These ECUs are interconnected via in-vehicle networks to ensure safety, assist drivers, and perform various functions. A controller area network (CAN) facilitates real-time data transmission between ECUs. CAN provides sufficient reliability for communication within vehicles. Modern vehicles also utilize vehicle-to-everything (V2X) technologies. V2X enables vehicles to link to external networks, facilitating communication with other vehicles, infrastructures, and electronic devices. Modern vehicles’ enhanced functionality and connections make them more susceptible to cyberattacks on both in-vehicle and external networks because of their extensive attack surfaces. To address this issue, this research presents a novel data-driven intrusion detection system (IDS) designed for in-vehicle and external communication networks. The proposed IDS can effectively detect many types of intrusions beyond the constraints of binary classifications. This system underwent a comprehensive evaluation using three in-vehicle datasets: car-hacking, car-hacking: attack & defense Challenge, and the ROAD datasets. Additionally, it was evaluated on two external communication network datasets: CICIDS2017 and CSE-CIC-IDS2018. The evaluation results demonstrated the IDS’s adaptability, achieving significant accuracy, recall, and F1 scores. It attained up to 99.99% accuracy with the in-vehicle datasets. Moreover, it exhibited an exceptionally low false-positive rate, as low as 0, in the Car-Hacking dataset. The proposed IDS architecture is a lightweight approach compared to state-of-the-art models. It significantly reduced complexity in terms of Floating-Point Operations per Second (FLOPs), memory usage, and learning parameters, all without compromising performance.

In today’s world of complex cybersecurity threats, it is crucial for organizations to follow strong security frameworks. This research introduces a new way to combine the National Cybersecurity Authority’s Essential Cybersecurity Controls with the Saudi Cybersecurity Workforce Framework. Our goal is to connect regulated cybersecurity controls with practical workforce deployment. We use a systematic process to match tasks in the Saudi Cybersecurity Workforce Framework with the controls required by the National Cybersecurity Authority. This alignment helps organizations comply with these controls while making good use of their cybersecurity workforce. A key part of our work is creating an optimization model that identifies the best job roles needed under the Saudi Cybersecurity Workforce Framework to fully meet the Essential Cybersecurity Controls set by the National Cybersecurity Authority. By using optimization algorithms, our model offers a cost-effective and efficient way for organizations to comply with regulations. The findings show that certain job roles in the Saudi Cybersecurity Workforce Framework are crucial for covering many Essential Cybersecurity Controls, highlighting their strategic importance in cybersecurity operations. This study lays the groundwork for future research in cybersecurity workforce optimization and offers a framework for other organizations to adapt their cybersecurity strategies to meet specific regulatory requirements.